visenger/awesome-mlops

★ 13,898Audience · dataComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((Awesome MLOps))
    Learning
      Books
      Online courses
      Papers
    Tools
      Workflow management
      Feature stores
      Model serving
    Operations
      Monitoring
      Testing
      Infrastructure
    Governance
      Responsible AI
      Compliance
      Community

mindmap root((Awesome MLOps)) Learning Books Online courses Papers Tools Workflow management Feature stores Model serving Operations Monitoring Testing Infrastructure Governance Responsible AI Compliance Community

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Things people build with this

USE CASE 1

Find books, courses, and papers to learn MLOps from scratch or fill gaps in specific areas like feature stores or model monitoring.

USE CASE 2

Discover open-source toolkits for workflow management, model serving, and infrastructure for your machine learning team.

USE CASE 3

Explore governance and responsible AI resources when building ML pipelines that need auditing, fairness checks, or compliance documentation.

Getting it running

Difficulty · easy Time to first run · 5min

In plain English

This repository is a curated reading list for MLOps, which stands for Machine Learning Operations. MLOps is the practice of building, deploying, monitoring, and maintaining machine learning models in production environments, treating AI development with the same engineering discipline applied to software. The list is organized into roughly twenty sections covering the full lifecycle. These include foundational resources on what MLOps is and why it matters, books and online courses for people learning the field, workflow management tools, feature stores (systems for storing and sharing the data inputs models use), data engineering practices, model deployment and serving, testing and monitoring of live models, and infrastructure considerations. There are also sections on governance and responsible AI, the economics of machine learning, community forums, and newsletters. Each section is a numbered list of links pointing to external resources: papers, blog posts, course pages, books on O'Reilly and similar platforms, open-source toolkits, and community organizations. The list does not explain the resources in depth but acts as a starting index you can browse to find what is relevant to your role or question. The collection is maintained by Dr. Larysa Visengeriyeva and follows the 'awesome list' convention common on GitHub, where curated link collections for a specific topic are published openly for the community to use and contribute to. If you are new to machine learning in production and want to know where to look, this is a broad map of the field's learning resources rather than a tool or library you install and run. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

I'm a data scientist who wants to move our team's models into production. Based on the awesome-mlops list, what workflow management and model serving tools should I evaluate first, and what is the key difference between them?

Prompt 2

I need to set up monitoring for a churn prediction model that is already deployed. Point me to the model monitoring section of awesome-mlops and explain what the three most important things to track are.

Prompt 3

We are building a feature store for our recommendation system. Walk me through what a feature store does and which tools from the awesome-mlops list are worth evaluating for a mid-size team.

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